The number of robots around the world is increasing rapidly. And it's said that automation will threatening more than 800m jobs worldwide by 2030. In the UK, it's claimed robots will replace 3.6m workers by this date, which means one in five British jobs would be performed by an intelligent machine. Jobs in higher education are no exception – with recent studies showing a rapid advancement in the use of these technologies in universities. The full potential of these disruptive technologies is yet to be discovered, but their impact on teaching and learning is expected to be huge.
A prized attribute among law enforcement specialists, the expert ability to visually identify human faces can inform forensic investigations and help maintain safe border crossings, airports, and public spaces around the world. The field of forensic facial recognition depends on highly refined traits such as visual acuity, cognitive discrimination, memory recall, and elimination of bias. Humans, as well as computers running machine learning (ML) algorithms, possess these abilities. And it is the combination of the two--a human facial recognition expert teamed with a computer running ML analyses of facial image data--that provides the most accurate facial identification, according to a recent 2018 study in which Rama Chellappa, Distinguished University Professor and Minta Martin Professor of Engineering, and his team collaborated with researchers at the National Institute of Standards and Technology and the University of Texas at Dallas. Chellappa, who holds appointments in UMD's Departments of Electrical and Computer Engineering and Computer Science and Institute for Advanced Computer Studies, is not surprised by the study results.
The history of AI is often told as the story of machines getting smarter over time. What's lost is the human element in the narrative, how intelligent machines are designed, trained, and powered by human minds and bodies. In this six-part series, we explore that human history of AI--how innovators, thinkers, workers, and sometimes hucksters have created algorithms that can replicate human thought and behavior (or at least appear to). While it can be exciting to be swept up by the idea of super-intelligent computers that have no need for human input, the true history of smart machines shows that our AI is only as good as we are. In the 1970s, Dr. Geoffrey Franglen of St. George's Hospital Medical School in London began writing an algorithm to screen student applications for admission.
Two MIT alumnae and three current MIT doctoral students are among this year's 30 recipients of The Paul and Daisy Soros Fellowships for New Americans. The five students -- Joseph Maalouf, Indira Puri, Grace Zhang, Helen Zhou, and Jonathan Zong -- will each receive up to $90,000 to fund their doctoral educations. The newest fellows were selected from a pool of 1,767 applications based on their potential to make significant contributions to U.S. society, culture, or their academic fields. The P.D. Soros Fellowships are open to all American immigrants and children of immigrants, including DACA recipients, refugees, and asylum seekers. In the past nine years, 34 MIT students and alumni have been awarded this fellowship.
Robots are well-known for being very good at some very specific things. They're often defined by words like "precision" and "repeatability" and "speed," because if you want a robot to be uniquely useful, it's usually going to have to leverage one or more of those characteristics in a way that makes it better at some specific task than humans are. Robots have been doing this for decades, typically in places like industrial settings, but things are starting to change, and roboticists are beginning to look towards other applications in more unconstrained, dynamic environments, like non-industrial settings. Such environments (our homes, for example) are the kinds of places that we really, really want robots to be useful in. We want them doing our chores so that we don't have to, ideally without causing catastrophic damage or injury at the same time.
A team led by 22-year-old Anne Dattilo, an undergraduate student at the University of Texas, Austin, discovered two planets, officially named K2-293b and K2-294b. A team led by 22-year-old Anne Dattilo, an undergraduate student at the University of Texas, Austin, discovered two planets, officially named K2-293b and K2-294b. A team of astronomers led by an undergraduate student in Texas has discovered two planets orbiting stars more than 1,200 light-years from Earth. Astronomers already knew of about 4,000 exoplanets, so finding two more might not seem like huge news. But it's who found them and how that's getting attention.
A panel of experts has called for all university and technical college students in Japan to be given beginner-level education on artificial intelligence. The proposal is part of a package of AI-related ideas presented by the panel at the day's meeting of the government's innovation promotion council, headed by Chief Cabinet Secretary Yoshihide Suga. The proposals, released Friday, are expected to be reflected in a comprehensive innovation policy package, which will be drawn up around June, and an AI strategy, to be formulated by summer. The panel called for having all university and technical college students take beginner-level programs on math, data science and AI, and letting half acquire the skills to apply AI to their own fields of study. It also asked the government to provide working adults with opportunities to learn such AI skills.
What skills, ideas, and experiences should students expect to leave college with? The annual celebration of learning is named after the late Margaret MacVicar, the first dean for undergraduate education and the founder of the Undergraduate Research Opportunities Program (UROP). Vice Chancellor Ian Waitz hosted the afternoon's festivities and began by introducing the 2019 MacVicar Faculty Fellows: Ford Professor of Economics Joshua Angrist, computer science professor Erik Demaine, anthropology professor Graham Jones, and comparative media studies professor T.L. Taylor. Each was honored for their contributions to undergraduate education and selected through nominations from their colleagues and students. This year, four faculty members and three students were asked to present three-minute lightning talks on what is important to today's learners.
Freed from a variety of tasks by artificial intelligence, doctors will have more time with patients, Topol predicts. In 1970 in The New England Journal of Medicine, William Schwartz predicted that by the year 2000, much of the intellectual function of medicine could be either taken over or at least substantially augmented by "expert systems"--a branch of artificial intelligence (AI). Schwartz hoped that the medical school curriculum would be "redirected toward the social and psychologic aspects of health care" and that medical schools would attract applicants interested in "behavioral and social sciences and … the information sciences and their application to medicine." But Schwartz's dream of smart medical technologies, for the most part, remains just that. Eric Topol, however, is optimistic about the future of health care.
Government usually isn't the place to look for innovation in IT or new technologies like artificial intelligence. But Ott Velsberg might change your mind. As Estonia's chief data officer, the 28-year-old graduate student is overseeing the tiny Baltic nation's push to insert artificial intelligence and machine learning into services provided to its 1.3 million citizens. "We want the government to be as lean as possible," says the wiry, bespectacled Velsberg, an Estonian who is writing his PhD thesis at Sweden's Umeå University on using the Internet of Things and sensor data in government services. Estonia's government hired Velsberg last August to run a new project to introduce AI into various ministries to streamline services offered to residents.